Abstract: Based on adjacent biannual remote sensing imageries and the associated subcompartment data of last year, automated developing techniques for the subcompartment data was presented in this study. By correlation analysis of multi temporal remote sensing features and vegetation change,certain remote sensing features that might make a greater contribution to the identification of actual vegetation changes were picked up. Combining these selected remote sensing features with prior knowledge of historical subcompartment GIS data, the discrimination method for forest resources changes was established through synthetically analysis, and then the changed boundaries for each subcompartment were extracted by both edge extracting and image segmentation separately method. Subcompartment edges were finally clipped with the generated boundaries,and the forest resource data updating of this year was finished. The results indicated that improper certain pseudo boundaries exist and true boundaries lost when the edge extraction techniques emploved. However,change information extracted by image segmentation algorithms presented a satisfactory result in accordance with the production requirements.The result derived from image segmentation methodologies agree well with those obtained by artificial visual delineation.